Fine coordinate attention for surface defect detection.

Surface defect detection remains a challenging task due to issues such as inconspicuous targets, significant variations among identical defects, and minimal differences between distinct defects. To address these challenges, a Fine Coordinate Attention (FCA) block is proposed in this paper, which encodes both average and salient information in two coordinate directions, so that the spatial dependence can be captured and the long-range interaction can be achieved. And such localization-friendly information is crucial for industrial surface defect images with subtle targets. Specifically, the FCA block can recalibrate feature maps of a surface defect image through three steps: coordinate information aggregation, cross-dimension interaction, and attention generation. It can be embedded into any convolutional neural network (CNN) structure to improve performance. Additionally, two resistance spot welding (RSW) surface defect datasets are published in this paper: an image classification dataset RSW-C and an object detection dataset RSW-D. Experimental results for image classification and object detection demonstrate that the FCA block outperforms existing attention mechanisms. The code is available at , while the two RSW datasets can be found at www.kaggle.com/datasets/alfredzimmer/rswdatasets. [ABSTRACT FROM AUTHOR]